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How To Query Worker Data using allorad

How to Query Worker Data using allorad

What You'll Learn

  • Essential commands for querying worker data and performance metrics
  • How to check worker registration status and retrieve inference history
  • Understanding worker scores, regret calculations, and network participation
  • Troubleshooting worker issues using diagnostic queries

Overview

Below is a list of commands to understand how to pull information about workers via allorad:

Why Query Worker Data?

Worker data queries help you:

  • Monitor performance: Track your worker's scores and inference accuracy
  • Troubleshoot issues: Diagnose registration problems or missed rewards
  • Optimize operations: Understand how your worker impacts network outcomes
  • Validate setup: Confirm proper registration and active participation

Prerequisites

  • allorad CLI: Command-line interface for Allora Network
  • A basic understanding of the Allora Network: Familiarity with workers, topics, and inference concepts
  • Access to RPC endpoints: Network connectivity to query blockchain data
  • Worker addresses: Know the specific worker addresses you want to query

Query Command Structure

These functions read from the appchain only and do not write. Add the Command value into your query to retrieve the expected data.

Base Query Format:

allorad q emissions [Command] --node <RPC_URL>

Command Components:

  • allorad q emissions: Base query structure for emissions module
  • [Command]: Specific function to execute (see individual commands below)
  • --node <RPC_URL>: RPC endpoint for network connection

Worker Registration Commands

Check if Worker is Registered in a Topic

Query Details:

  • RPC Method: IsWorkerRegisteredInTopicId
  • Command: is-worker-registered [topic_id] [address]
  • Description: Checks whether a worker is registered in a specific topic. It returns true if the worker is registered in the given topic, and false otherwise.

Positional Arguments:

  • topic_id: The identifier of the topic where you want to check the worker's registration status.
  • address: The address of the worker you want to check.

Use Case

Why use it?

  • This command is essential if you want to verify whether a worker is properly registered in a specific topic before submitting inferences or participating in a topic's operations.

Example Scenario:

  • Before deploying a worker to submit inferences on a particular topic, you can confirm that the worker is registered to that topic to ensure proper functionality and avoid errors.

Practical Applications:

  • Pre-deployment validation
  • Troubleshooting inference submission failures
  • Verifying topic participation eligibility

Performance Analysis Commands

Get Worker Inferences Scores at Block

Query Details:

  • RPC Method: GetWorkerInferenceScoresAtBlock
  • Command: inference-scores [topic_id] [block_height]
  • Description: Return scores for a worker at a block height.

Positional Arguments:

  • topic_id: Identifier of the topic whose information will be returned.
  • block_height: Block height to query.

Use Case

Why use it?

  • You may want to verify if a worker has received a high score at a specific block, particularly if you're troubleshooting worker rewards or performance discrepancies.

Example Scenario:

  • If you believe your worker's reward for a particular topic is inaccurate, use this command to view how it was scored at a specific block.

Practical Applications:

  • Reward verification and dispute resolution
  • Performance trend analysis over time
  • Score correlation with inference accuracy

Inference Data Commands

Get Latest Worker Inference By Topic ID

Query Details:

  • RPC Method: GetWorkerLatestInferenceByTopicId
  • Command: worker-latest-inference [topic_id] [worker_address]
  • Description: Gets the latest inference for a given worker and topic.

Positional Arguments:

  • topic_id: Identifier of the topic whose information will be returned
  • worker_address: Given worker to query on

Use Case

Why use it?

  • This command is useful if you want to check whether a worker is actively submitting inferences for a topic and how recent those inferences are.

Example Scenario:

  • A worker has missed rewards, and you want to verify if their latest inference was successfully submitted on time for a given topic.

Practical Applications:

  • Verify active participation in topics
  • Check inference submission timing
  • Troubleshoot missed reward scenarios

Worker Information Commands

Get Worker Node Info

Query Details:

  • RPC Method: GetWorkerNodeInfo
  • Command: worker-info [address]
  • Description: Get node info for a specified worker node.
    • Returns the owner address of the worker node.
    • Returns the worker node address being queried.

Positional Arguments:

  • address: The address of the worker node whose information will be retrieved.

Use Case

Why use it?

  • This command is helpful for checking the current status of a worker node, especially if you are managing multiple nodes and want to verify the ownership or troubleshoot node configuration.

Example Scenario:

  • You want to ensure the node you've set up is operating under the correct owner and is correctly registered on the network.

Practical Applications:

  • Multi-node management and verification
  • Ownership validation for security purposes
  • Node configuration troubleshooting

Advanced Analytics Commands

Get Naive Inferer Network Regret

Query Details:

  • RPC Method: GetNaiveInfererNetworkRegret
  • Command: naive-inferer-network-regret [topic_id] [inferer]
  • Description: Returns the network regret associated with including an inferer's naive inference in a batch for a given topic. If no specific regret is calculated, the command defaults to the topic's InitialRegret value.

Positional Arguments:

  • topic_id: The identifier of the topic for which the regret will be calculated.
  • inferer: The address of the inferer whose naive inference is being evaluated.

Use Case

Why use it?

  • Use this command to assess the regret associated with incorporating an inferer's naive inference into a batch. Useful for analyzing how poorly an inference may perform within the context of the network's aggregate inference for a topic.

Example Scenario:

  • If you want to understand how an inferer's baseline performance impacts the network outcome, this command helps quantify that penalty.

Practical Applications:

  • Performance impact analysis
  • Inference quality assessment
  • Network contribution measurement

Get One-Out Inferer-Inferer Network Regret

Query Details:

  • RPC Method: GetOneOutInfererInfererNetworkRegret
  • Command: one-out-inferer-inferer-network-regret [topic_id] [one_out_inferer] [inferer]
  • Description: Returns the network regret when the implied outcome of the one_out_inferer is included in a batch alongside the inferer. If no specific regret value exists, it defaults to the topic's InitialRegret.

Positional Arguments:

  • topic_id: The identifier of the topic for which the regret will be calculated.
  • one_out_inferer: The address of the inferer whose implied inference is being evaluated.
  • inferer: The address of the inferer to compare against.

Use Case

Why use it?

  • This command is useful when comparing how two inferers impact the network when their inferences are processed together. It helps identify the potential penalty on network performance when adding a specific inferer to a batch.

Example Scenario:

  • You might want to compare the impact of two inferers to see how their joint performance influences the overall network regret. This is particularly useful for optimizing inference strategies.

Practical Applications:

  • Comparative performance analysis
  • Multi-worker impact assessment
  • Inference strategy optimization

Common Use Cases

Troubleshooting Workflow

  1. Check Registration: Use is-worker-registered to verify topic participation
  2. Verify Latest Inference: Use worker-latest-inference to confirm recent submissions
  3. Analyze Performance: Use inference-scores to review reward calculations
  4. Assess Impact: Use regret commands to understand network contribution

Performance Monitoring

  • Regular Health Checks: Monitor inference submission frequency and timing
  • Score Tracking: Track performance scores across different blocks and topics
  • Regret Analysis: Understand how your worker affects overall network accuracy

Multi-Worker Management

  • Ownership Verification: Use worker-info to confirm node ownership
  • Performance Comparison: Compare regret values between different workers
  • Resource Optimization: Identify top-performing workers for resource allocation

Next Steps

Build and Deploy a ForecasterQuery EMA Score of a Worker using allorad